This invention relates generally to data mining software.
Data mining software extracts knowledge that may be suggested by a set of data. For example, data mining software can be used to maximize a return on investment in collecting marketing data, as well as other applications such as credit risk assessment, fraud detection, process control, medical diagnoses and so forth. Typically, data mining software uses one or a plurality of different types of modeling algorithms in combination with a set of test data to determine what types of characteristics are most useful in achieving a desired response rate, behavioral response or other output from a targeted group of individuals represented by the data. Generally, data mining software executes complex data modeling algorithms such as linear regression, logistic regression, back propagation neural network, Classification and Regression (CART) and Chi.sup.2 (Chi squared) Automatic Interaction Detection (CHAID) decision trees, as well as other types of algorithms on a set of data.
Results obtained by executing these algorithms can be expressed in a variety of ways. For example, an RMS error, R.sup.2 value, confusion matrix, gains table or multiple lift charts or a single lift chart with multiple lift curves. Based on these results the decision maker can decide which model (i.e., type of modeling algorithm and learning parameters) might be best for a particular use.